NVIDIA
8 items across 4 editions · appeared in the last 2 editions in a row. First seen Fri 11 Sep, last seen Tue 15 Sep.
- TechCrunch, publishing on 14 September at 2:51 PM PDT, reports that Trump called Nvidia chief executive Jensen Huang while Huang was on stage at the All-In Summit in Los Angeles on Monday morning, and that Huang put the president on speakerphone for the audience. The panel had been discussing Dario Amodei's call to slow the pace of capability gains.
- TechCrunch quotes Trump saying "We're not going to let that happen. It's a hoax", and Huang replying "You're right. We're not going to let that happen, sir."
- NBC News, publishing at 7:06 PM EDT on 14 September, quotes Trump on the call: "The robots will not be taking over. The AI will not be taking over the rest of the world", "The whole thing is a hoax" and "Data centers are great, and they make people wealthy". NBC quotes Huang saying "We're going to make sure that everybody wins in the AI race in America."
- Neither report carries a White House readout or an Nvidia statement beyond what was said on stage. TechCrunch separately noted Nvidia's stock is up 33% over the past year but fell a few percentage points on the day.
- SemiAnalysis, publishing on 14 September, writes: "At GTC 2026, Jensen presented this graph that VR NVL72 achieved 3x performance per MW compared to Blackwell on O(1-3 Trillion) parameter model around 200 TPS. But when compared to the real world performance of Rubin already on prelease software, we are already seeing up to 7x better token throughput per megawatt."
- It reports that in the realistic 60–100 tokens-per-second operating range, "Vera Rubin achieves between 1.4x and 3x the throughput per TCO compared to the latest and greatest GB300 TRTLLM configuration", and that "Vera Rubin achieves approximately 61% higher maximum P90 interactivity than GB300 Dynamo TRTLLM, reaching 276.24 versus 171.53 P90 TPS". It adds that with the open-source SGLang stack, "GB300 can achieve similar interactivity as Vera Rubin".
- SemiAnalysis models "$159.5 billion in annual revenue and $149.9 billion in modeled profit per all-in utility GW" at 75 TPS and 60% utilisation, about "39% more revenue and 42% more modeled profit" than GB300 Dynamo SGLang, and says the rack tested is "the production SKU of 2300W TDP & 1.5TB of CPU LPDDR5X per compute tray".
- The measurements are on pre-release software, and SemiAnalysis thanks "Jensen Huang, Ian Buck, Nick Comly, Kedar Potdar, Rohit Nagraj, and the Mainland China TensorRTLLM Team" for help with the software bring-up and for verifying the benchmark results, so this is not an arm's-length test.
- Quartz, writing on 14 September and citing The Information's reporting, says Palantir, Nvidia and Booz Allen Hamilton are restricting or threatening to drop advanced models from Anthropic and OpenAI unless the labs provide stronger data protections: Palantir has pressed Anthropic for guarantees of zero data retention, Nvidia restricts Anthropic's models to less sensitive internal tasks in favour of its own Nemotron models, and Booz Allen has forbidden staff from running Anthropic's commercial model on cybersecurity projects that touch proprietary data.
- Quartz traces the dispute to a 30-day data retention policy Anthropic introduced in June with the rollout of Fable 5, which the company said it needed "to detect sophisticated attacks that unfold across multiple sessions" and would not use for training.
- Tom's Hardware, relaying the same report, says a large US utility company cancelled plans to test Fable — it had wanted to know whether the model could run core power infrastructure — after Anthropic refused a nonrevocable zero data retention policy, that Northrop Grumman runs open-source models on its own air-gapped servers instead, and that Novo Nordisk uses Claude but bans proprietary data from it.
- Quartz reports Anthropic's answer is Enterprise Frontier Safeguards, which lets enterprise customers keep activity data in their own Amazon S3, Azure Blob Storage or Google Cloud Storage under their own keys, with automated monitoring and no human review by Anthropic staff, rolling out in phases with broader availability targeted for later this autumn. The originating report is The Information's, which we could not open.
- The Associated Press reports that on Monday 14 September SoftBank Group fell 10.7% in Tokyo, SK Hynix and Kioxia Holdings each fell 6.4%, Samsung Electronics fell 4.1%, TSMC fell 1.2% and Tokyo Electron fell 1%. South Korea's Kospi lost 3.3% to 6,684.37 and Japan's Nikkei 225 slid 0.8% to 63,492.99, while Hong Kong's Hang Seng rose 0.4% to 24,904.46.
- Reuters reports that in US premarket trade at 04:46 a.m. ET, Nasdaq 100 e-minis were down 505.5 points or 1.72%, S&P 500 e-minis down 53.25 points or 0.70% and Dow e-minis down 97 points or 0.18%. Nvidia fell more than 2%, Intel nearly 6%, Marvell Technology around 6% and AMD around 5%, while Meta and Amazon each fell more than 1%. ServiceNow rose 3%, and Adobe and Workday 2.5% each.
- The AP quotes Dan Baker of Morningstar saying the decline "probably reflects the possibility that AI development may be slowed by regulators to try to avoid the worst case outcomes".
- These are intraday and premarket moves, not closing prices, and the pacing debate was not the only thing moving markets: the AP reports Brent crude rose 2.8% to US$107.55 a barrel in the same session.
- The paper, posted to arXiv on 9 September 2026 and announced in the 11 September listing, reports that the system "scored 30 out of 42 points at IMO 2026, reaching the gold-medal threshold".
- The abstract states the pipeline "operates entirely in natural language, with no formal prover, external tools, or internet access", using three Nemotron 3 Ultra checkpoints — the general-availability model and two post-trained specialists — in "an iterative search that generates, verifies, and refines candidate proofs", with a separate high-compute stage selecting each submission.
- The authors say they release both post-trained checkpoints plus the training data, training and inference code, the submitted solutions, and "Nemotron-IMO-Bench, a new benchmark of 200 novel olympiad-level problems".
- The arXiv abstract page carries no affiliation block; Hugging Face lists the institution as NVIDIA. The IMO score is the authors' own report of their own submission and is not peer reviewed.
- Reuters reported on 11 September at 8:46 pm that Nvidia is in talks to invest up to $10 billion as an anchor investor in Anthropic's IPO, which is seeking to raise up to $100 billion at a valuation of around $2 trillion, with completion expected before the US midterm elections in November.
- For comparison, Reuters cites Anthropic's May round of $65 billion raised at a $965 billion post-money valuation, and an annualised revenue run rate that surpassed $65 billion by the end of July, up from roughly $9 billion at the end of 2025.
- The report notes Nvidia said in November 2025 it would invest up to $10 billion in Anthropic under a broader partnership including a $30 billion Azure computing commitment, and that Anthropic committed more than $100 billion over a decade to AWS in April.
- Reuters says "The plans remain under negotiation and could change". Both companies declined to comment or did not respond, and no filing has been made. This is a Reuters exclusive; other outlets are aggregating it.
- Reported 10 September, sourced to the New York Times: the Department of Justice is examining whether Nvidia's roughly $20 billion arrangement with Groq, disclosed in late December 2025 and billed as a nonexclusive licensing agreement rather than an acquisition, was structured to sidestep merger review. Groq founder and then-CEO Jonathan Ross moved to Nvidia along with several key team members.
- The deal produced the Groq 3 language processing unit, now in full production as part of Nvidia's LPX rack-scale platform, integrating Groq's low-latency inference silicon into Nvidia's AI factory architecture.
- The inquiry sits alongside an FTC examination of acqui-hires across big tech. FTC Chairman Andrew Ferguson said in February that regulators want to ensure such deals "are not an attempt to get around" merger review. A finding against Nvidia would put a widely copied AI-industry deal template at risk.
- This is an investigation, not a complaint. No charges have been filed, and the underlying NYT report is behind a paywall; details here are as relayed by SDxCentral.
- Announced 10 September: Skild AI's S1 learns new manipulation tasks from a single video demonstration using in-context learning, with no weight updates or task-specific retraining. NVIDIA reports roughly 66% per-step success on multistep tasks against about 9% for comparable systems, and that one video example is worth roughly 380 hands-on training examples — 50 to 100 hours of manual collection.
- S1 executes unfamiliar tasks up to 10 minutes long across dozens of steps, including potting plants, making pancakes, pour-over coffee and kit assembly. In one plant-potting test, the gap from recording the demonstration to autonomous execution on hardware was 11 minutes.
- Skild reports a $100 million annual revenue run rate ten months after launch and more than 60 deployment partnerships across manufacturing, logistics, inspection, security and food preparation. If the demonstration-efficiency claim holds outside curated tasks, the cost of teaching a robot a new job falls by orders of magnitude — which is the labour-substitution variable to watch.
- These are vendor figures published on a supplier's blog, not an independent benchmark. "Per-step" success is not end-to-end task success, and the 66% versus 9% comparison does not name the baseline systems.